Beiwe: A data collection platform for high-throughput digital phenotyping

Onnela, Jukka‐Pekka; Dixon, C.F.; Griffin, Keary; Jaenicke, Tucker; Minowada, Leila; Esterkin, Sean; Siu, Alvin; Zagorsky, Josh; Jones, Eli

doi:10.21105/joss.03417

Cited by 49 publications

(29 citation statements)

References 21 publications

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“…We collected smartphone data through Beiwe, an open-source, end-to-end encrypted high-throughput digital phenotyping platform [ 22 ]. It consists of Android and iOS smartphone apps for data collection and an Amazon Web Services cloud-based system back-end for data collection and processing [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Beukenhorst¹,

Burke²,

Scheier³

et al. 2022

JMIR Mhealth Uhealth

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Background Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term. Objective The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis. Methods We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer. Results Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%). Conclusions These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful. Trial Registration ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

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Section: Methodsmentioning

confidence: 99%

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Beukenhorst¹,

Burke²,

Scheier³

et al. 2022

JMIR Mhealth Uhealth

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“…Methods Clinical severity ratings of speech recordings from 12 people with dysarthria due to amyotrophic lateral sclerosis have been collected to date. The speakers recorded themselves reading a short passage using the Beiwe smartphone research platform (Onnela et al, 2021). Offline, speaking rate was extracted and the recordings were combined with multispeaker babble at a -1-dB signal-to-noise-ratio.…”

Section: Discussionmentioning

confidence: 99%

Abstracts of the 8th International Conference on Speech Motor Control Groningen, August 2022

SSTP

2022

SSTP

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This Supplement of ‘Tijdschrift voor Stem-, Spraak- en Taalpathologie’ (Journal of Voice, Speech and Language Pathology) contains the abstracts of the eighth edition of the International Conference on Speech Motor Control, held in Groningen, The Netherlands, August 24 - 27, 2022. With this eighth conference, a well-established Nijmegen (5 editions) - Groningen (6th & 7th edition) tradition continues. This conference, like the ones before, highlights new trends and state-of-the-art approaches in theoretical and applied research in the area of normal and disordered speech motor control. The five years since the previous conference in 2017 have yielded not only further insights in genetic, neural, physiological and developmental aspects of speech production, stuttering and other speech motor conditions, but have also advanced theoretical modelling. Combined with ongoing studies of populations that are increasingly better characterized genetically and neurobiologically, this quantitative boost of interdisciplinary results is now leading to a qualitative turning point in which large data sets are analyzed with powerful artificial intelligence and machine learning algorithms. The implementation of theories into computational models allows for the explicit testing of multifactorial interactions, thereby going beyond the more traditional single-factor experiments. Machine learning and the data sharing required to make this feasible are a special topic of the 2022 conference.

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“…Beiwe is an open-source digital phenotyping research platform designed specifically for the collection of research-grade, raw (unprocessed) data from smartphone sensors, logs and self-entry measures (surveys, voice recordings) 48 . Data collected with the Beiwe app are encrypted and stored on the smartphone until Wi-Fi transfer to the HIPAA-compliant Amazon Web Services (AWS) cloud occurs.…”

Section: Methodsmentioning

confidence: 99%

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

et al. 2023

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Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2–4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

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Beiwe: A data collection platform for high-throughput digital phenotyping

Cited by 49 publications

References 21 publications

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Abstracts of the 8th International Conference on Speech Motor Control Groningen, August 2022

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

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